DRSN-based liquid-based thin-layer cell smear digital pathological image detection method

Abstract

The invention discloses a DRSN-based liquid-based thin-layer cell smear digital pathological image detection method. The method comprises the following steps: (1) inputting a liquid-based thin-layer cell smear image file; (2) carrying out variable step size sliding window sampling on the image file; (3) filtering and preprocessing the sampled image to obtain a focus area, and adjusting a sliding step length in real time through a sampling image preprocessing threshold value; (4) inputting a focus area into a classification model for prediction, and obtaining confidence; and (5) filtering out an area with low confidence or presenting an area with high confidence through a threshold value. The deep residual shrinkage network is used for detecting the digital pathological image data of the liquid-based thin-layer cell smear, the method is applied to early screening of cell pathological cancers, such as screening of cervical squamous cell carcinoma, and compared with a traditional microscopic examination method, the method has the advantages that the film reading time and the detection cost can be saved, and the diagnosis and treatment accuracy and efficiency can be improved.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a liquid-based thin-layer cell smear digital pathological image detection method based on DRSN. Background technique [0002] With the emergence of convolutional neural networks and the perfection of deep neural networks, artificial intelligence computer vision based on deep learning has developed rapidly in recent years. Li Feifei, a tenured professor of computer science at Stanford University, once said that the level of artificial intelligence can now begin to affect the medical and health field. make a contribution. [0003] Cervical cancer is one of the common gynecological malignancies, and its incidence rate ranks second among female malignancies, second only to breast cancer. Cervical cancer is the only malignant tumor with a clear etiology in the world, and persistent high-risk HPV infection is the main factor causing cervical cancer. Among...

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Problems solved by technology

But now the shallow network (shallower network) can not significantly improve the recognition effect of the network, so the problem to be solved is how to solve the problem of gradient disappearance in the case of deepening the network

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